Data reliability is a major concern in seismic data interpretation since the choice of a drilling location and the likelihood that a drilling operation will be successful is based on the accuracy and reliability of the data used to make drilling-related decisions. The estimation and display of random noise is therefore one of most important factors in data reliability assessment. This assessment, in turn, can be used in reservoir engineering studies.
In the field of seismic exploration there are a number of techniques for noise analysis that have been proposed. One approach that provides for noise estimation in a 3-D post-stacked display of seismic data is described in Dash, B and K. A. Obaidullah, “Determination of signal and noise statistics using correlation theory”, Geophysics, 1970, 35, 24-32 (“Dash and Obaidullah”). Assuming an image signal is correlated from trace to trace and noise is not correlated, Dash and Obaidullah applied correlation theory to deduce signal power from cross-correlation and total power from auto-correlation. The power of noise is then extracted from the difference. The shortcoming is that seismic traces are not correlated when crossing a fault or nonconformity. This method of noise estimation is therefore biased by geological structures.
The method described by Potter and Roden uses horizontal component records to estimate noise in a vertical direction. The method only works for strong directional noise and does not apply to 3-D post-stacked data. There are usually three basic steps in image noise variance estimation. These are: (1) image structure suppression, (2) local variance estimation and (3) global variance estimation. The most important step among them is image structure suppression. Potter, T. F. and R. B. Roden, “Seismic noise estimation using horizontal components”, Geophysics, 1999, No. 4, pp. 617-632. Rank et al. discloses the use of a simple two-tap difference filter in both the horizontal and vertical directions to suppress the image structure. It has been found that the filters should be cascaded for better results. However, this simple two-tap FIR filtering also leaves a lot of edge information in the filtered images, so that special post-processing is required in the global variance estimation stage to correct for noise variance. Rank, K., M. Lendi and R. Unbehauen, 1999, “Estimation of Image Noise Variance,” IEE Proc.-Vis. Image Signal Process., Vol. 146, No. 2, pp. 80-84.
In order to better suppress an image structure in two dimensions, the use of the difference of two Laplacian filters as the mask to filter the image is described in J. Immerkaer, “Fast Noise Variance Estimation”, Computer Vision and Image Understanding, 1996, Vol. 64, No.2, pp. 300-302 (“Immerkaer)”. There is no suggestion in the Immerkaer article for a 3-D mask or means for smoothing to remove the anisotropic effects of the Immakaer mask.
The following patents disclose methods for noise reduction in an image containing image and noise which is not relevant to the reliability of the data. For example, U.S. Pat. No. 5,461,655 discloses a method and apparatus for noise reduction in the context of medical radiography imaging. Published application US 20040066978 discloses an image processing method and image processing apparatus for use in connection with medical imaging. The disclosures of U.S. Pat. Nos. 7,085,426 and 7,130,484 describe the use of Volterra filters for enhancement of contours in noisy images, e.g., in medical applications and in the x-ray scanning for weapons transports. Published application US 20020012472 discloses a method for visualization of time sequences of 3-D optical fluorescence microscopy images, and specifically a method for compressing 4-D image data to accelerate its visualization (see FIGS. 3-6).
A method for sharpness enhancement in the display of TV images employing a specified filter is disclosed in U.S. Pat. No. 6,847,738. Finally, U.S. Pat. No. 5,844,627 discloses a structure and method for reducing spatial noise in processing digital video signals.
The prior art methods of these patents do not improve on the ability to recognize valid structure in the presence of random noise. It is therefore an object of the present invention to provide an assessment of the reliability of derived seismic attributes based on random noise estimation.